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        <a class="list-group-item list-group-item-action" href="#list-item-1"><b>1. Introduction</b></a>
        <a class="list-group-item list-group-item-action" href="#list-item-2"><b>2. CNIT Online Input</b></a>
        <a class="list-group-item list-group-item-action" href="#list-item-3"><b>2.1 Submit requirement</b></a>
        <a class="list-group-item list-group-item-action" href="#list-item-4"><b>2.2 How to submit the RNA
          transcript sequences?</b></a>
        <a class="list-group-item list-group-item-action" href="#list-item-5"><b>3. CNIT Results Output</b></a>
        <a class="list-group-item list-group-item-action" href="#list-item-6"><b>3.1 CNIT results: html view</b></a>
        <a class="list-group-item list-group-item-action" href="#list-item-7"><b>3.2 CNIT results details</b></a>
        <a class="list-group-item list-group-item-action" href="#list-item-8"><b>4. FAQ</b></a>
        <a class="list-group-item list-group-item-action" href="#list-item-9"><b>The framework of CNIT</b></a>
        <a class="list-group-item list-group-item-action" href="#list-item-10"><b>The global prediction for CNIT</b></a>
        <a class="list-group-item list-group-item-action" href="#list-item-11"><b>What's new in CNIT?</b></a>
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        <h4 id="list-item-1">1. Introduction</h4>
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        <p>
          CNIT (Coding-NonCoding Identifying Tool) software is a powerful signature tool to effectively
          distinguish between protein-coding and non-coding sequences by profiling adjoining nucleotide triplets
          ANT based on sequence intrinsic composition, especially for classification of incomplete transcripts and
          sense-antisense transcript pairs. Last version of CNCI1 is widely used by worldwide researchers. For
          better serve for scientific community and to make users distinguish transcripts more conveniently, we
          update CNIT to CNIT. It can discriminate the coding and non-coding transcripts faster, more accurately,
          in more species, especially for plants.
          <a href="/CNIT/">Here</a>, we provide an online version of CNIT.
        </p>
        <h4 id="list-item-2">2. CNIT Online Input</h4>
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        <p>CNIT accepts RNA transcript sequences in fasta format.</p>
        <h4 id="list-item-3">2.1 Submit requirement</h4>
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        <p>Fasta format:</P>
        <p>Size requirement: Less than 10000 lines in input box and no line limitation in batch model. Maximum
          allowable upload file size is 50 Mb.</P>
        <p>Name requirement: Sequence names beginning with ‘>’ symbol are required.</P>
        <p>Sequence requirement: Only characters in DNA and RNA sequences are case ignoring, such as ATCGUatcgu.</P>
        <p>GTF format are supported when you install the CNIT standalone version on most Linux-based operating
          systems not in Web temporarily.</P>
        <h4 id="list-item-4">2.2 How to submit the RNA transcript sequences?</h4>
        <hr/>
        <P>There are two ways to submit RNA transcript sequences:</p>
        <P>1) Paste RNA sequences in fasta format into the big input box at the home page.</p>
        <P>2) Upload fasta file by the batch operation.</p>
        <h4 id="list-item-5">3. CNIT Results Output</h4>
        <hr/>
        <p>The results will be stored on our server for seven days, you can retrieve your result via the job-ID
          link.</p>
        <h4 id="list-item-6">3.1 CNIT results: html view</h4>
        <hr>
        <p>CNIT results html view gives an overview of coding status of the input sequences. Each row corresponds to
          one input sequence. The columns show the Transcript ID, the coding/noncoding classification label
          (Index), the coding probability score (CNIT Score) The results can be copied, printed and downloaded
          directly from our server in different file formats such Excel, PDF, csv.</p>
        <hr/>
        <h4 id="list-item-7">3.2 CNIT results details</h4>
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        <p>Results details consist of the most-like CDS (MLCDS) region detail (Figure A), Sequence (Figure B) and
          CNIT Score Detail Plot: Red line represents the correct transcriptional reading frame and other five
          lines (blue or green) represent other five reading frames, green line indicates the distribution of the
          coverage (the right y-axis) of the MLCDS region for each protein-coding transcript across the normalized
          length, here, we show identification result of coding and noncoding sequence sample (Figure C).
          Moreover, you blast your sequence in NONCODE database in this page directly.</p>
        <h4 id="list-item-8">4. FAQ</h4>
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        <h4 id="list-item-9">The framework of CNIT</h4>
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        <p>The top panel shows the process of a sequence in a testing set. For a given sequence, six MLCDS regions
          (represented by six lines) are identified from six reading frames (represented by six color arrow lines)
          using a sliding window and dynamic programming algorithm. Then, an MLCDS region with a maximal S-score
          is selected to incorporate into an Xgboost. The bottom panel shows the training and classification
          process. Reliable protein-coding and non-coding sequences are used as a training set, and four features
          including 67 values are extracted to train Xgboost, which classifies the incorporating sequence into
          protein-coding or non-coding sequence.</p>
        <h4 id="list-item-10">The global prediction for CNIT</h4>
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        <p>Here, we showed the prediction of CNIT for 37 species (11 animal species, 26 plant species) with the
          corresponding AUC value.</p>
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        <h4 id="list-item-11">What's new in CNIT?</h4>
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        <p>In comparison with CNCI, CNIT runs∼200 times faster than CNIT and exhibits more accurate in more species,
          especially for plants, when using Human and Arabidopsis data as training sets. Because CNIT can classify
          protein-coding and non-coding RNAs solely based on sequence intrinsic composition as CNCI, it is
          potentially applicable to a variety of species without whole-genome sequence or with poorly annotated
          information. The last but not the least, the updated model in CNIT can identify the most species in
          existing software of distinguish transcripts coding probability.</p>
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